Interval-fuzzy-quadratic Programming Based On Water Quality Simulation Model And Its Application To Water Quality Management

In view of the current deterioration of water quality in water environment problems, it is significant to carry out the effective water quality management and reasonable pollution emission planning to improve water quality. Inherent uncertainties in river water pollution control problems cause great difficulties in relevant modeling processes. A superiority-inferiority interval-quadratic programming (SIFQP) method is developed for supporting source-oriented pollution control under uncertainty. SIFQP is capable of tackling uncertainties expressed as interval numbers, fuzzy sets, and their combinations. Moreover, it can deal with nonlinearities in the objective function. An interactive algorithm and an improved superiority-inferiority method with high computational efficiency are provided to solve the SIFQP model. The proposed method has been applied to a real case of water pollution control for Xiangxi River, with an objective of minimizing system cost for wastewater treatment while restricting environmental impacts to an acceptable level. Three scenarios are analyzed in order to make in-depth analysis of interactions between water-quality requirement and economic implication. Multiple solutions for wastewater treatment practices have been generated corresponding to varied risk levels of water quality violations, which could help screen optimal alternatives according to decision makers’profit and risk considerations as well as various system conditions. Decision makers aim to low system cost that may imply a high risk of violating environmental criteria. Willingness to accept high system cost will guarantee the water quality requirements. Therefore, the results can help local decision markers identify desired strategies for water quality protection of the study river under multiple uncertainties.